1. Field of the Invention
This invention is directed to methods and apparatus for analyzing a selected region of interest in medical image data of a subject.
2. Description of the Prior Art
In the medical imaging field, several imaging schemes are known. For example PET (Positron Emission Tomography) is a method for imaging a subject in 3D using an injected radio-active substance which is processed in the body, typically resulting in an image indicating one or more biological functions.
The definition of regions or volumes of interest (ROI/VOI) is a typical precursor to quantitative analysis of medical images, such as nuclear medicine emission images (for example, PET or SPECT). Such regions may be defined around areas of high intensity which correspond to high tracer uptake (hotspots). For example, in FDG-PET images for oncology; such areas may be indicative of the presence of a tumor. Oncology physicians frequently annotate lesions in PET scans for the purpose of making a diagnosis, or for use in radiotherapy. The mean or maximum tracer uptake can aid a reader in determining the likelihood of cancer.
Radiologists and nuclear medicine clinicians regularly read a large number of clinical PET studies each day. Traditionally, clinical decisions were made on the basis of a largely qualitative assessment of PET scans; however, there is a strong drive towards a more standardized, quantitative assessment. This drive is highlighted by the recent publication of the proposed PERCIST criteria for evaluating PET response (“From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors”—Wahl, 2009, JNM. 50(5) 122s-150s). This PERCIST criteria can be regarded as the PET, or functional imaging, equivalent of the RECIST criteria of anatomical response, which focuses on changes in size of a lesion.
In brief the PERCIST criteria recommends measuring SUL (lean body mass-corrected SUV) peak for up to 5 lesions (with a maximum of 2 per organ). SUL peak for a lesion is measured by positioning a spherical ROI of 1 cm3 volume within the lesion so as to maximize the mean SUL within the ROI. To be considered a lesion by the PERCIST criteria, each hotspot must have a SUL peak (from a pre-treatment scan) at least 1.5×mean liver SUL+2 standard deviations of the mean SUL (from a 3 cm diameter spherical ROI positioned in the right hepatic lobe). If the liver is diseased, then 2.0×blood-pool activity+2 standard deviations in the mediastinum is suggested as the minimal metabolically measurable tumor activity (MMMTA). Of course, the specific parameters here may change in future versions of the PERCIST criteria, or related standards.
A number of manufacturers provide software for reviewing clinical PET imaging data (e.g., TrueD from Siemens Healthcare). These applications provide a variety of tools to aid the clinician in their review of a case, including ROI-drawing tools. However, currently-available ROI tools still require considerable input from the clinician and are not designed specifically to support a PERCIST-style evaluation. Clinician input will typically be precise delineation of an ROI or of a bounding region for an ROI along with a threshold value.
The requirements for quantitative evaluation outlined in the PERCIST criteria clearly place an added burden on the reporting clinician in terms of ROI creation and reporting. The present invention aims to address this additional burden and the associated problems, and provide improvements upon the known methods.
In general terms, an embodiment of a first aspect of the invention provides a method of analyzing a selected region of interest in medical image data of a subject, including obtaining an initial image data set; filtering the initial image data set to generate a filtered data set, wherein the step of filtering comprises computing, for each voxel of the initial image data set, a value of intensity for a standardized volume of interest centered on that voxel; registering a user selection of a region of interest in the initial image data set; and computing from the filtered image data set a value of intensity for the selected region of interest.
This allows a fast means of computing the required standardized intensity value for reference with the selected region of interest.
Preferably, the value of intensity for the standardized volume of interest is a value of mean SUL. More preferably, the standardized volume of interest is a 1 cm3 spherical volume of interest. Still more preferably, the value of intensity for the selected region of interest is the local maximum SUL for the region of interest.
In an embodiment, the step of registering the user selection of the region of interest includes obtaining a list of voxels of the initial image data set sorted according to intensity; registering a user-selection of an initial voxel in the initial image data set; and selecting, as the region of interest, at least one voxel from the sorted list according to a property of the at least one voxel in relation to the user-selected initial voxel.
Suitably, the step of selecting the region of interest further includes determining a set of candidate regions of interest in the initial image data set; and determining a hierarchy among the set of candidate regions according to intensity, wherein the region of interest selected is one of the candidate regions.
Preferably, the step of computing the value of intensity for the region of interest includes determining a region of the filtered image data set corresponding to the selected region of interest in the initial image data set; and determining the voxel in the filtered image region having the maximum intensity value, and returning that maximum intensity value as the value of intensity for the selected region of interest.
Suitably, the step of determining the region of the filtered image data set includes obtaining a list of voxels of the filtered image data set sorted according to intensity, determining a set of candidate regions of interest in the filtered image data set, and determining a hierarchy among this set of candidate regions according to intensity, wherein each filtered set candidate region is associated with a local maximum value of intensity.
In an embodiment, the method further includes pre-processing the respective image data set to generate the list of voxels of the image sorted according to intensity; and following generation of the list, registering the user selection of the initial voxel.
In another embodiment, the region selected in the initial image data set includes the user-selected voxel. In still another embodiment, the region selected in the initial image data set is a region associated with the closest local maximum intensity value to the user-selected voxel.
Preferably, the hierarchy of candidate regions is generated by a connected-component algorithm.
In one embodiment, the method further includes displaying the initial image data set; and displaying with the initial image data set the computed value of intensity for the selected region of interest.
Suitably, the property of the at least one voxel is intensity, and wherein the minimal metabolically measurable tumor activity is set as a threshold in relation to the user-selected voxel.
Preferably, the list is obtained for those voxels of the respective image data set having values for intensity greater than the minimal metabolically measurable tumor activity
Suitably, the method further comprises: determining a hepatic or mediastinal region of interest in the initial image data set; and calculating the minimal metabolically measurable tumor activity from a measurement of intensity in the hepatic or mediastinal region of interest.
An embodiment of a second aspect of the invention provides an apparatus for analyzing a selected region of interest in medical image data of a subject, that includes a processor adapted to: obtain an initial image data set; filter the initial image data set to generate a filtered data set, wherein the step of filtering comprises computing, for each voxel of the initial image data set, a value of intensity for a standardized volume of interest centered on that voxel; register a user selection of a region of interest in the initial image data set; and compute from the filtered image data set a value of intensity for the selected region of interest; and a display device for displaying the initial image data set and the computed value of intensity for the selected region of interest.
An embodiment of a third aspect of the invention provides a method of analyzing a selected region of interest in medical image data of a subject captured by a medical imaging apparatus, the method comprising: obtaining, by a processor, an initial image data set; filtering, by processor, the initial image data set to generate a filtered data set, wherein the step of filtering includes computing, for each voxel of the initial image data set, a value of intensity for a standardized volume of interest centered on that voxel; registering, by a processor, a user selection of a region of interest in the initial image data set; computing, by a processor, from the filtered image data set a value of intensity for the selected region of interest; and displaying on a display device the initial image data set and the computed value of intensity for the selected region of interest.
The invention also encompasses a non-transitory computer-readable storage medium encoded with program code that, when the program code is loaded into or run on a computer, causes the computer to implement a method as described above and/or to become an apparatus as described above.
The above aspects and embodiments may be combined to provide further aspects and embodiments of the invention.